

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							''''''
			__lowerCamelCase										=							(
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			__lowerCamelCase										=							None  # compression type in fsspec. ex: "gzip"
			__lowerCamelCase										=							None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


			def __init__(     self   ,   lowercase = ""   ,   lowercase = None   ,   lowercase = None   ,   **lowercase							)	->    int:
							'''simple docstring'''






							super().__init__(self   ,   **UpperCAmelCase_							)
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							A__          =      fsspec.open(
							    UpperCAmelCase_   ,   mode="rb"   ,   protocol=UpperCAmelCase_   ,   compression=self.compression   ,   client_kwargs={
							        "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
							        "trust_env": True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop("client_kwargs"   ,   {}							),  # To avoid issues if it was already passed.
							    }   ,   **(target_options or {})   ,   )
							A__          =      os.path.basename(self.file.path.split("::"							)[0]							)
							A__          =      (
							    self.compressed_name[: self.compressed_name.rindex("."							)]
							    if "." in self.compressed_name
							    else self.compressed_name
							)
							A__          =      None


			@classmethod
			def   UpperCamelCase		(     cls   ,   lowercase							)	->    List[Any]:
							'''simple docstring'''






							return super()._strip_protocol(UpperCAmelCase_							).lstrip("/"							)


			def   UpperCamelCase		(     self							)	->    Optional[Any]:
							'''simple docstring'''






							if self.dir_cache is None:
											A__          =      {**self.file.fs.info(self.file.path							), "name": self.uncompressed_name}
											A__          =      {f["name"]: f}


			def   UpperCamelCase		(     self   ,   lowercase							)	->    Any:
							'''simple docstring'''






							return self.file.open().read()


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase = "rb"   ,   lowercase=None   ,   lowercase=True   ,   lowercase=None   ,   **lowercase   ,   )	->    Tuple:
							'''simple docstring'''






							A__          =      self._strip_protocol(UpperCAmelCase_							)
							if mode != "rb":
											raise ValueError(F'Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''							)
							return self.file.open()

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							'''bz2'''
			__lowerCamelCase										=							'''bz2'''
			__lowerCamelCase										=							'''.bz2'''

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							'''gzip'''
			__lowerCamelCase										=							'''gzip'''
			__lowerCamelCase										=							'''.gz'''

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							'''lz4'''
			__lowerCamelCase										=							'''lz4'''
			__lowerCamelCase										=							'''.lz4'''

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							'''xz'''
			__lowerCamelCase										=							'''xz'''
			__lowerCamelCase										=							'''.xz'''

class        a__    (       _lowerCAmelCase      ):
			"""simple docstring"""







			__lowerCamelCase										=							'''zstd'''
			__lowerCamelCase										=							'''zstd'''
			__lowerCamelCase										=							'''.zst'''


			def __init__(     self   ,   lowercase   ,   lowercase = "rb"   ,   lowercase = None   ,   lowercase = None   ,   lowercase = DEFAULT_BLOCK_SIZE   ,   **lowercase   ,   )	->    int:
							'''simple docstring'''






							super().__init__(
							    fo=UpperCAmelCase_   ,   mode=UpperCAmelCase_   ,   target_protocol=UpperCAmelCase_   ,   target_options=UpperCAmelCase_   ,   block_size=UpperCAmelCase_   ,   **UpperCAmelCase_   ,   )
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							A__          =      self.file.__enter__

							class        a__    :
										"""simple docstring"""







										def __init__(     self   ,   lowercase							)	->    Union[str, Any]:
														'''simple docstring'''






														A__          =      file_


										def __enter__(     self							)	->    Union[str, Any]:
														'''simple docstring'''






														self._file.__enter__()
														return self


										def __exit__(     self   ,   *lowercase   ,   **lowercase							)	->    List[Any]:
														'''simple docstring'''






														self._file.__exit__(*UpperCAmelCase_   ,   **UpperCAmelCase_							)


										def __iter__(     self							)	->    Optional[int]:
														'''simple docstring'''






														return iter(self._file							)


										def   UpperCamelCase		(     self							)	->    Any:
														'''simple docstring'''






														return next(self._file							)


										def __getattr__(     self   ,   lowercase							)	->    Union[str, Any]:
														'''simple docstring'''






														return getattr(self._file   ,   UpperCAmelCase_							)

							def fixed_enter(*lowercase   ,   **lowercase							):
											return WrappedFile(_enter(*UpperCAmelCase_   ,   **UpperCAmelCase_							)							)

							A__          =      fixed_enter



"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       torch.FloatTensor





class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				int							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				Tuple[str] = ("DownEncoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[str] = ("UpDecoderBlock2D",)							,   UpperCAmelCase_			:				Tuple[int] = (64,)							,   UpperCAmelCase_			:				int = 1							,   UpperCAmelCase_			:				str = "silu"							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				int = 256							,   UpperCAmelCase_			:				int = 32							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				float = 0.18215							,   UpperCAmelCase_			:				str = "group"							,   )->	Optional[Any]:







              """simple docstring"""
              super().__init__()

              # pass init params to Encoder
              UpperCamelCase       			=		Encoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   down_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   double_z=UpperCAmelCase_							,   )

              UpperCamelCase       			=		vq_embed_dim if vq_embed_dim is not None else latent_channels

              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )
              UpperCamelCase       			=		VectorQuantizer(UpperCAmelCase_							,   UpperCAmelCase_							,   beta=0.25							,   remap=UpperCAmelCase_							,   sane_index_shape=UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Convad(UpperCAmelCase_							,   UpperCAmelCase_							,   1      )

              # pass init params to Decoder
              UpperCamelCase       			=		Decoder(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   up_block_types=UpperCAmelCase_							,   block_out_channels=UpperCAmelCase_							,   layers_per_block=UpperCAmelCase_							,   act_fn=UpperCAmelCase_							,   norm_num_groups=UpperCAmelCase_							,   norm_type=UpperCAmelCase_							,   )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	VQEncoderOutput:







              """simple docstring"""
              UpperCamelCase       			=		self.encoder(UpperCAmelCase_      )
              UpperCamelCase       			=		self.quant_conv(UpperCAmelCase_      )

              if not return_dict:
                     return (h,)

              return VQEncoderOutput(latents=UpperCAmelCase_      )






       @apply_forward_hook
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              # also go through quantization layer
              if not force_not_quantize:
                     UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.quantize(UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		h
              UpperCamelCase       			=		self.post_quant_conv(UpperCAmelCase_      )
              UpperCamelCase       			=		self.decoder(UpperCAmelCase_							,   quant if self.config.norm_type == "spatial" else None      )

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				bool = True      )->	Union[DecoderOutput, torch.FloatTensor]:







              """simple docstring"""
              UpperCamelCase       			=		sample
              UpperCamelCase       			=		self.encode(UpperCAmelCase_      ).latents
              UpperCamelCase       			=		self.decode(UpperCAmelCase_      ).sample

              if not return_dict:
                     return (dec,)

              return DecoderOutput(sample=UpperCAmelCase_      )





